Motion processing includes the use of motion vectors in both motion estimation and motion compensation. Motion estimation is the process of determining motion vectors. The motion vectors describe the transformation of objects from one two dimensional image to another, commonly from adjacent frames or pictures in a video sequence. Motion compensation is the process of applying the determined motion vectors to objects in one picture in order to synthesize the transformation of the described objects to a subsequent picture in the video sequence. The combination of motion estimation and motion compensation is a key part of video compression and often is highly demanding in terms of processing costs.
The motion vectors in motion processing are determined by methods which may be categorized as either direct or indirect. In practice, direct methods relying on pyramidal and block-based searches are typically used in video encoders. Direct methods often require increases to processing power and processing costs in order to increase the accuracy and/or precision of motion vectors determined by these methods.
Indirect methods for determining motion vectors often use statistical functions, applied over a local or global area in a picture, to identify matches between estimated movement occurring in the pictures and generated motion vectors. Fidelity metrics are commonly utilized in attempting to identify and remove false matches which do not correspond to actual motion. However, fidelity metrics often lead to opportunistic best matches, which are errors, and motion vector outliers, which are inefficient as they require more bits to code. These limitations tend to reduce video compression quality and efficiency.
Furthermore, existing evaluation methods, in relying on fidelity metrics, tend to favor high contrast regions in a picture. This often produces poor motion estimates for regions of low texture, and commonly leads to noticeably incorrect motion in these low textures. Also, fidelity metrics often fail to discriminate motion that occurs during changes within a video sequence to contrast, brightness, blur, added noise, artifacts, and other differences which can occur during fades, dissolves, and compression. These other limitations also tend to reduce video compression quality and efficiency.
The weaknesses of fidelity metrics in any of these circumstances may often be alleviated by increasing the motion processing power, which raises processing costs. Nevertheless, in circumstances in which fidelity metrics are less effective, motion processing using existing evaluation methods often requires a trade-off between achieving more accurate/precise motion vectors in video compression and lower processing costs.